The present invention relates to a method and apparatus for determining the cerebral state of a patient. One application of the method and apparatus is determining the extent of a hypnotic state of the patient resulting, for example, from the administration of an anesthetic agent. That extent is often termed the xe2x80x9cdepth of anesthesia.xe2x80x9d In the method and apparatus of the present invention, changes in the cerebral state can be accurately and quickly determined.
In a simplistic definition, anesthesia is an artificially induced state of partial or total loss of sensation or pain, i.e. analgesia. For most medical procedures the loss of sensation is accompanied by a loss of consciousness on the part of a patient so that the patient is amnestic and is not aware of the procedure.
The xe2x80x9cdepth of anesthesiaxe2x80x9d generally describes the extent to which consciousness is lost following administration of an anesthetic agent. As the magnitude of anesthetization, or depth of anesthesia, increases, an anesthetized patient typically fails to successively respond to spoken commands, loses the eyelid reflex, loses other reflexes, undergoes depression of vital signs, and the like.
While loss of consciousness (hypnosis) and the loss of sensation (analgesia) are significant features of anesthesia, it should be noted that balanced high quality anesthesia must also consider muscle relaxation, suppression of the autonomous nervous system, and blockade of the neuro muscular junction. Sufficient muscle relaxation is required to ensure optimal operating conditions for the surgeon manipulating the patient""s tissue. The autonomous nervous system, if not suppressed, causes the patient to respond to surgical activity with a shock reaction that effects heavily on hemodynamics and the endocrine system. To keep the patient motionless, the neuro muscular junctions transmitting orders from the brain to the muscles of the body may be blocked so that the body of the patient becomes paralyzed.
While the need to determine the state of all five components of anesthesia is widely recognized, ascertaining and quantifying the state of hypnosis or depth of anesthesia in a reliable, accurate, and quick manner has been, and is, the subject of extensive attention. One reason for this is its importance. If the anesthesia is not sufficiently deep, the patient may maintain or gain consciousness during a surgery, or other medical procedure, resulting in an extremely traumatic experience for the patient, anesthesiologist, and surgeon. On the other hand, excessively deep anesthesia reflects an unnecessary consumption of anesthetic agents, most of which are expensive. Anesthesia that is too deep requires increased medical supervision during the surgery recovery process and prolongs the period required for the patient to become completely free of the effects of the anesthetic agent. A second reason for the continuing study and attention being given to monitoring the hypnotic condition of a patient arises because of its difficulty: that is, anesthetic agents alter the activity and state of the patient""s brain and these changes are not always easy to detect.
A measure of the depth of anesthesia that may be used for research purposes is found in an Observer""s Assessment of Alertness and Sedation or OAAS. The OAAS determines the level of consciousness or, conversely, the depth of sedation or anesthesia, based on a patient""s response to external stimuli. One such assessment that classifies the depth of anesthesia in six levels, is summarized by the table below. The transition from consciousness to unconsciousness may be deemed to occur when the OAAS score changes from level 3 to level 2. Level zero corresponds to a state of deep anesthesia in which the patient shows no response to a very painful stimulus.
xe2x80x9cPtosisxe2x80x9d is a drooping of the upper eyelids. xe2x80x9cTOF stimulationxe2x80x9d (xe2x80x9ctrain-of-fourxe2x80x9d) is a very short, painful electrical (50 mA) stimulus applied to the ulnar nerve in the arm of the patient, repeated four times to evaluate the intensity of muscular contraction. In xe2x80x9ctetanic stimulationxe2x80x9d the electrical current (50 mA) is applied continuously for a period of time, such as 5 seconds. The ulnar nerve is the nerve which, when pinched, gives rise to the well known xe2x80x9ccrazy or funny bonexe2x80x9d effect.
While useful for research purposes, an OAAS scale provides only a limited number of scaling levels and is limited in practical use because of the attention required from the anesthesiologist and the use of painful stimuli.
It has long been known that the neurological activity of the brain is reflected in biopotentials available on the surface of the brain and on the scalp. Thus, efforts to quantify the extent of anesthesia induced hypnosis have turned to a study of these biopotentials. The biopotential electrical signals are usually obtained by a pair, or plurality of pairs, of electrodes placed on the patient""s scalp at locations designated by a recognized protocol and a set, or a plurality of sets or channels, of electrical signals are obtained from the electrodes. These signals are amplified and filtered. The recorded signals comprise an electroencephalogram or EEG.
Among the purposes of filtering is to remove electromyographic (EMG) signals from the EEG signal. EMG signals result from muscle activity of the patient and will appear in electroencephalographic electrodes applied to the forehead or scalp of the patient. They are usually considered artifacts with respect to the EEG signals.
A typical EEG is shown in FIG. 1. A macro characteristic of EEG signal patterns is the existence of broadly defined low frequency rhythms or waves occurring in certain frequency bands. Four such bands are traditionally recognized: Delta (0.5-3.5 Hz), Theta (3.5-7.0 Hz), Alpha (7.0-13.0 Hz), and Beta (13.0-32.0 Hz). Alpha waves are found during periods of wakefulness and may disappear entirely during sleep. The higher frequency Beta waves are recorded during periods of intense activation of the central nervous system. The lower frequency Theta and Delta waves reflect drowsiness and periods of deep sleep. Even higher frequency EEG patterns than those described above have been investigated, although measurements are difficult due to very low amplitudes of these high-frequency waves.
By analogy to the depth of sleep, it can be said that an overall slowing down of the EEG takes place as the depth of anesthesia increases, while the magnitude of the signal initially often increases. However, this gross characterization is too imprecise and unreliable to use as an indication of such a critical medical aspect as the extent of hypnosis. The foregoing circumstance has led to the investigation and use of other techniques to study EEG waveforms to ascertain the underlying condition of the brain, including the depth of anesthesia to which a patient is subjected. It will be immediately appreciated from FIG. 1 that EEG signals are highly random in nature. Unlike other biopotential signals, such as those of an electrocardiogram (ECG), an EEG normally has no obvious repetitive patterns, the morphology and timing of which can be conveniently compared and analyzed. Nor does the shape of the EEG waveform correlate well to specific underlying events in the brain. Hence, except for certain phenomena, such as epileptic seizures, which are readily apparent from visual inspection of an EEG, the indication of other conditions in the brain in the EEG is much more subtle.
Prefatory to the use of other techniques, the EEG signals are subjected to analog to digital signal conversion by sequentially sampling the magnitude of the analog EEG signals and converting same to a series of digital data values. The sampling is typically carried out at a rate of 100 Hz or greater. The digital signals are stored in the magnetic or other storage medium of a computer and then subjected to further processing to ascertain the underlying state of the brain. Such processing typically uses sets of sequential EEG signal samples or data points representing a finite block of time, commonly termed an xe2x80x9cepoch.xe2x80x9d The analysis of the data is usually carried out on a moving average basis employing a given epoch and a certain number of backward epochs.
Some of the techniques by which EEG signals can be analyzed in an effort to determine the depth of anesthesia are well described in Ira J. Rampil, A Primer for EEG Signal Processing in Anesthesia, Vol. 89, Anesthesiology No. 4, pgs. 980 et seq., October 1998.
One such technique is to examine, in some meaningful way, how the voltage of an EEG signal changes over time. Such an analysis is termed a xe2x80x9ctime-domain analysis.xe2x80x9d However, an EEG signal is not a deterministic signal This means that it is not possible to exactly predict future values of the EEG from past values in the manner that, for example, the shapes of past QRS complexes in an ECG signal can be used to predict future values for analytical and diagnostic purposes. Rather, it is a stochastic signal for which certain statistical characteristics of the signal can be predicted.
Time-domain based analysis is useful in the study and quantification of burst suppression in the EEG. During deep anesthesia, the EEG time-domain signal may develop a pattern of activity which is characterized by alternating periods or xe2x80x9cburstsxe2x80x9d of normal, or high frequency and amplitude, voltage signals and periods of low or no voltage, which periods are termed those of xe2x80x9csuppression.xe2x80x9d The extent of this phenomenon can be expressed as a xe2x80x9cburst suppression ratio (BSR)xe2x80x9d which is a time domain EEG parameter describing the time the EEG voltage is in the suppressed state as a fraction of the sampling period.
A second approach to analyzing EEG waveforms examines signal activity as a function of frequency, i.e. a xe2x80x9cfrequency-domain analysis.xe2x80x9d It has long been recognized that complex waveforms, such as EEG signals, can be decomposed, or transformed, into a plurality, or spectrum, of simple sine or cosine waves of various frequencies, amplitudes, and phases. Frequency-domain spectra can be obtained from sequential time-domain EEG signal data by a Fourier transform. Frequency-domain analysis analyzes the spectrum of frequency signals obtained from the transform to determine characteristics and features occurring in wave forms having the various frequencies of the spectrum. The components of the resulting Fourier transformation corresponding to any particular frequency are complex numbers which can be characterized by an amplitude and a phase value. The amplitude information is typically graphically displayed as a power versus frequency histogram in which frequency is graphed on the abscissa and power, which corresponds to the amplitude squared, is graphed on the ordinate.
Further efforts to obtain useful information from electroencephalograms have employed higher order analyses, including the bispectrum and trispectrum. The bispectrum, which measures the correlation of phase between two different frequency components and quantifies the relationships among the underlying sinusoidal components of the EEG, has received considerable attention. The bispectrum specifically quantifies the relationship between sinusoids at two primary frequencies f1 and f2 and a modulation component at the frequency f1+f2. A strong phase relationship between f1, f2 and f1+f2 creates a large bispectral value for frequency f1+f2.
For clinical use, it is desirable to simplify the results of EEG signal analysis of the foregoing, and other types, into a workable parameter that can be used by an anesthesiologist in a clinical setting when attending the patient. Ideally, what is desired is a simple, single parameter or index that quantifies the depth of anesthesia on a consistent, continuous scale extending from full alertness to maximally deep, but reversible, hypnosis. To be fully useful such a scale should maintain its consistency, notwithstanding the differing pharmacological effects of different anesthetic agents, as well as the differing physiologies of different patients.
Various such parameters for relating EEG signal data to the hypnotic state of the patient are discussed in the literature. Several use frequency domain power spectral analysis. These parameters include peak power frequency (PPF), median power frequency (MPF), and spectral edge frequency (SEF). A peak power frequency (PPF) parameter uses the frequency in a spectrum at which occurs the highest power in the sampled data as an indication of the depth of anesthesia. The median power frequency (MPF) parameter, as its name implies, uses the frequency that bisects the spectrum. In the same fashion, the spectral edge frequency uses the highest frequency in the EEG signal. A modification of the latter is the SEF 95 parameter, which is the frequency below which 95% of the power in the spectrum resides.
To improve the consistency of an indicator of the hypnotic state or depth of anesthesia, several parameters may be employed in combination. For example, the spectral edge frequency (SEF) parameter may be combined with the time-domain burst suppression ratio (BSR) parameter to extend its use to the deepest regimen of anesthesia.
While parameters of the foregoing types can detect changes in the EEG caused by anesthetic agents and hence are useful in determining the depth of anesthesia, they suffer from an inability to be calibrated to behavioral endpoints, as well as from sensitivity to the different EEG patterns induced by different anesthetic agents.
More complex combinations of parameters are described in U.S. Pat. Nos. 4,907,597; 5,010,891; 5,320,109; and 5,458,117 to Nassib Chamoun or Chamoun et al. and are employed in the anesthesia monitor product made and sold by the assignee of the patents, Aspect Medical Systems of Framingham, Mass. The patents describe various combinations of a time-domain subparameter and frequency-domain subparameters, including a high order spectral subparameter, to form a single variable that correlates behavioral assessments of sedation and hypnosis over a range of anesthesia for several anesthetic agents. Because of this ability, the Aspect Medical Systems product has found clinical acceptance.
In the form implemented in the product, the parameter, called bispectral index, BIS, consists of the following four subcomponents: SyncFastSlow, BetaRatio, Burst Suppression (BSR), and xe2x80x9cQUAZIxe2x80x9d. The calculation of the subparameter SyncFastSlow utilizes bispectral analysis in the frequency-domain. The SyncFastSlow parameter corresponds to the logarithm of the ratio of the sum of all bispectral peaks in the frequency range 0.5-47 Hz divided by the sum in the range 40-47 Hz. The BetaRatio subparameter gives the logarithm of the power ratio in the frequency ranges 30-47 Hz and 11-20 Hz. It is a frequency-domain parameter that has been found to work best in light sedation. As noted above, in very deep levels of anesthesia, EEG signal contains data samples in which the EEG activity is suppressed. The Burst Suppression Ratio obtained from a time-domain analysis of the EEG signal describes the relative content of burst and suppression in the signal. The Burst Suppression Ratio is operative in deep anesthesia in which the suppression occurs. The parameter xe2x80x9cQUAZIxe2x80x9d detects burst suppression when it is superimposed on very slow waves, ( less than 1 Hz). See Ira J. Rampil in Anesthesiology, supra.
The resulting bispectral index, BIS, is a combination of these four subparameters. The combining algorithm weights the different subparameters according to their range of best performance. While the details of the algorithm are unpublished and proprietary, it is known that different subparameters or combinations of subparameters are employed depending on the level of hypnosis or depth of anesthesia.
BetaRatio predominates in light anesthesia, SynchFastSlow in surgical levels, and BSR and QUAZI at deep levels of anesthesia. The function that determines the weights for these subparameters was originally developed by a statistical procedure by fitting the parameter to a behavioral scale using a large collected database of EEG measured during anesthesia.
While this approach is systematic and scientifically sound, it has inherent limitations. As the technique requires changing from one algorithm to another depending on the level of anesthesia, a question arises whether these changes are made consistently, or whether there are perhaps, problems at the boundaries of the BetaRatio-dominated, SynchFastSlow-dominated, and BSR/QUAZI-dominated regions.
Some recent studies point to the conclusion that such problems exist. In xe2x80x9cOnset of propofol-induced burst suppression may be correctly detected as deepening of anaesthesia by approximate entropy but not by bispectral indexxe2x80x9d, Br J Anaesth 2001 September; 87(3):505-7 by Bruhn et al, observations were reported in which BIS failed to show deepening anesthesia at the onset of burst suppression. In a subsequent study, accepted for publication in Journal of Clinical Monitoring and Computing, Bruhn et al. derived the functional dependence of BIS on BSR. Up to 40% suppression ratio, the average BIS values remained constant regardless of suppression ratios Beyond a suppression ratio  greater than =40%, BIS and suppression ratio were invariably linearly correlated, following the equation BIS=50xe2x88x92suppression ratio/2 The results indicated also that there is a range of values between 30 and 40 in which BIS is relatively insensitive to changes in drug concentration. It is plausible that such a region is required in order to switch smoothly from a xe2x80x9cSynchFastSlowxe2x80x9d-dominated algorithm to a xe2x80x9cBSR/QUAZI-dominatedxe2x80x9d algorithm. The problem may thus be a direct consequence of the way BIS is constructed. Detsch, et al., in xe2x80x9cIncreasing Isoflurane Concentration may cause Paradoxical Increases in the EEG bispectral index in Surgical Patientsxe2x80x9d, Br. J. Anaesth. 84 (2000), pgs. 33-37 reported paradoxical behavior of BIS at increasing isoflurane concentrations in approximately the same range of BIS values.
It is less clear whether similar problems are found at the higher boundary region of BIS between the xe2x80x9cBetaRatioxe2x80x9d- and xe2x80x9cSynchFastSlowxe2x80x9d-dominated ranges. The apparently discontinuous jump of BIS often seen at loss of consciousness may be related to a switching of the algorithm, but it may also be a consequence of a real state transition of the cortex that is reflected in the EEG signal.
Further, computation of the bispectral index (BIS) parameter requires averaging at least 15 seconds of EEG data. Thus, this index may be not sufficiently fast to detect changes in the state of a patient as is required in the clinical situation. See, Baker, et al. Electroencephalographic Indices Related to Hypnosis and Amnesia During Propofol Anaesthesia for Cardioversion, Anaesthesia and Intensive Care, Vol. 28, No. 4, 2000. This may cause a practical problem in the use of the BIS index. An anesthesiologist who has a measurement of anesthetic depth available, is likely to more precisely titrate the amount of anesthetic agent administered to a patient, and is thus likely to reduce the amount of agent administered in order to improve the state of the patient after recovery and to reduce costs. However, the lessened amount of anesthetic agent may increase the risk that the patient will awaken during surgery. It is therefore essential that an anesthesiologist knows immediately if a patient starts to approach consciousness out of the hypnotic state.
A different approach to the analysis of electroencephalographic signals is to attempt to quantify the regularity or irregularity of the highly random EEG signal for use as an indication of the depth of anesthesia. It is known that developmental factors such as maturation (John et al, Development Equations for the EEG, Science, 210, (1980) pgs. 1255-1258 and Alvarez et al., On the Structure of EEG Development, Electroenceph, Clin. Neurophysiol., 1989, 73:10-19) and attention (Dongier et al. Psychological and Psychophysiological States in A. Rxc3xa9mond (Ed), Handbook of Electroenceph. Clin. Neurophysiol., Vol. 6A, Elsevier, Amsterdam, 1976: pgs. 195-254) increase the irregularity of the EEG signal. Concentration on a particular mental task has been shown to result in a greater degree of local desynchronization of EEG (Pfurtscheller et al., Event-related EEG/MEG Synchronization and Desynchronization: Basic Principles, Clinical Neurophysiology 110 (1999) pgs. 1842-1857, Inoye et al. Quantification of EEG Irregularity by use of the Entropy of the Power Spectrum, Electro-encephalography and Clinical Neurophysiology, 79 (1991) pgs. 204-210). These findings suggest that an active cortex generally has a more irregular EEG patterns than an inactive cortex.
There are a number of concepts and analytical techniques directed to quantifying the irregularity and complex nature of random or stochastic signals, such as the EEG. One such concept is entropy. Entropy, as a physical concept, is proportional to the logarithm of the number of microstates available to a thermodynamical system, and is thus related to the amount of disorder in the system. When used in information theory and signal analysis, entropy addresses and describes the irregularity, complexity, or unpredictability characteristics of a signal. In a simple example, a signal in which sequential values are alternately of one fixed magnitude and then of another fixed magnitude has an entropy of zero, i.e. the signal is completely regular and totally predictable. A signal in which sequential values are generated by a random number generator has greater complexity and a higher entropy.
Applying the concept of entropy to the brain, the premise is that when the mind is full of activity the state of the brain is more complex. Since EEG signals reflect the underlying state of brain activity, this is reflected in relatively more xe2x80x9cirregularityxe2x80x9d or xe2x80x9ccomplexityxe2x80x9d in the EEG signal data, or, conversely, in a low level of xe2x80x9corder.xe2x80x9d As a person falls asleep or is anesthetized, the brain function begins to lessen and becomes more orderly and regular. As the activity state of the brain changes in such circumstances, it is plausible to consider that this will be reflected in the EEG signals by a relative lowering of the xe2x80x9cirregularityxe2x80x9d or xe2x80x9ccomplexityxe2x80x9d of the EEG signal data, or conversely, increasing xe2x80x9corderxe2x80x9d in the signal data.
With respect to anesthesia, an increasing body of evidence indeed shows that EEG signal data contains more xe2x80x9corderxe2x80x9d, i.e. less xe2x80x9cirregularityxe2x80x9d, and lower entropy, at higher concentrations of an anesthetic agent, i.e. greater depth of anesthesia, than at lower concentrations. At a lower concentration of anesthetic agent, the EEG signal has higher entropy. This is due, presumably, to lesser levels of brain activity in the former state than in the latter state. See xe2x80x9cStochastic complexity measures for physiological signal analysisxe2x80x9d by I. A. Rezek and S. J. Roberts in IEEE Transactions on Biomedical Engineering, Vol. 4, No. 9, September 1998 describing entropy measurement to a cut off frequency of 25 Hz and Bruhn, et al. xe2x80x9cApproximate Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect during Desflurane Anesthesiaxe2x80x9d, Anesthesiology, 92 (2000), pgs. 715-726 showing that approximate entropy of the EEG, measured in a frequency range of 0.5 to 32 Hz, closely follows the concentration of the anesthetic drug in the brain See H. Viertixc3x6-Oja et al. xe2x80x9cNew method to determine depth of anesthesia from EEG measurementxe2x80x9d in J. Clin. Monitoring and Comp. Vol. 16 (2000) pg. 16 and H. E. Viertixc3x6-Oja et al. xe2x80x9cEntropy of EEG signal is a robust index for depth of hypnosisxe2x80x9d, Anesthesiology 93 (2000) A, pg. 1369, which report that entropy behaves monotonously as a function of anesthetic depth evaluated by the OAAS scale and the transition from consciousness to unconsciousness takes place at a universal critical value of entropy which is independent of the patient.
One reason for the usefulness and success of the entropy concept may reside in the fact that it is a scale-invariant measure that is independent of the frequency and amplitude scales of the signal. The absolute frequencies of EEG rhythms are known to vary from patient to patient, and therefore techniques that use measures defined in terms of the absolute frequency scale, such as the spectral edge frequency, discussed above, may suffer from such variation among patients.
The pertinence of the concept of entropy to the conscious and unconscious states of the brain is also supported in recent theoretical work (see Steyn-Ross et al., Phys. Rev. E60 1999, pgs. 7229-7311) which applies thermodynamic theory to the study of the brain. The results of this work suggest that when a patient undergoing anesthetization passes from the conscious state to the unconscious state, a thermodynamic phase transition of the neural system of the brain takes place which is roughly analogous to the phase change occurring when water freezes into ice. During the process of freezing, an amount of thermodynamical entropy, proportional to the latent heat of the process, is removed so that water and ice have different entropies. According to the theory, the conscious and unconscious states of the brain should have distinct, different values of entropy. While thermodynamical entropy is conceptually different from the entropy in information theory, it is plausible to assume a close correlation between the two in this context. In a well-ordered anesthetized state the neurons are obviously likely to have more regular firing patterns that are reflected in a more regular EEG signal than in the more disordered awake state. If this theory is experimentally proven, it will lend further support to the concept of entropy as a fundamental characteristic of the cerebral state of the brain.
In sum, the following can be said. First, certain forms of entropy have generally been found to behave consistently as a function of anesthetic depth. This warrants consideration of entropy as a natural and robust choice to characterize levels of hypnosis. Also, because entropy correlates with depth of anesthesia at all levels of anesthesia, it avoids the need to combine various subparameters as in the bispectral index (BIS). Second, the transition from consciousness to unconsciousness takes place at a critical level of entropy which is independent of the patient. Thirdly, and of particular practical significance, recovery of a patient toward consciousness from anesthesia can often be predicted by a rise of entropy toward the critical level.
A number of techniques and associated algorithms are available for quantifying signal irregularity, including those based on entropy, as described in the Rezek and Roberts article in IEEE Transactions on Biomedical Engineering article, supra. One such algorithm is that which produces spectral entropy for which the entropy values are computed in frequency space. Another algorithm provides approximate entropy which is derived from the Kolmogorov-Sinai entropy formula and computed in Taken""s embedding space. See Steven M. Pincus, Igor M. Gladstone, and Richard A. Ehrenkranz, xe2x80x9cA regularity statistic for medical data analysisxe2x80x9d, J. Clin. Monitoring 7 (1991), pgs. 335-345. A program for computing approximate entropy is set out in the Bruhn et al., article in Anesthesiology. The spectral entropy and approximate entropy techniques have found use in analyzing the complexity of EEG signal data.
Another technique for non-linear analysis of highly random signals is expressed in Lempel-Ziv complexity in which the complexity of a string of data points is given by the number of bytes needed to make the shortest possible computer program which is able to generate the string. See Abraham Lempel and Jacob Ziv, xe2x80x9cOn the complexity of finite sequencesxe2x80x9d, IEEE Trans., IT-22 (1976) pgs. 75-81.
A still further approach that may be applied to EEG signal analysis is fractal spectrum analysis based on chaos theory. In fractal spectrum analysis, the EEG signal is divided into a harmonic component and a fractal component. The harmonic component includes the simple frequencies whereas the fractal component contains the part which is invariant under scaling in time. It has been found that the fractal exponent Beta which corresponds to the frequency power law 1/fxcex2 increases consistently in the course of deepening anesthesia (Viertixc3x6-Oja et al. J. Clin. Monitoring).
An object of the present invention is to provide an improved method and apparatus for accurately determining the cerebral state of a patient, including the hypnotic or consciousness state of a patient and the depth of anesthesia that a patient is experiencing.
A particular object of the present invention is to provide such a method/apparatus that can rapidly make such determinations, especially when a patient is emerging to the conscious state from unconsciousness.
The gist of the present invention is to combine an effective measure of the cerebral state of a patient derived from EEG signal data, preferably a complexity measurement such as spectral entropy or approximate entropy, with a more rapidly obtainable measure derived from EMG signal data and to use the combination as a state indication. The measure derived from the EMG signal data may comprise spectral power data. When used as an indication of the hypnotic state, or depth of anesthesia, of the patient, the measure derived from the EMG signal data enhances and confirms the determination of the hypnotic state made using the EEG signal data and renders predicting changes in the hypnotic state of the patient more rapid. This is of particular advantage in alerting an attending anesthesiologist to the possibility that an anesthetized patient may shortly regain consciousness so that the anesthesiologist can take timely, appropriate action.
Both the EEG and EMG signal data are typically obtained from the same set of electrodes applied, for example, to the forehead of the patient. The EEG signal component dominates the lower frequencies (up to about 30 Hz) contained in the biopotentials existing in the electrodes. At higher frequencies, EEG power decreases rapidly and exponentially. The EMG signal has a wide noise-like spectrum and dominates at frequencies higher than 30 Hz.
Sudden appearance of EMG signal data often indicates that the patient is responding to some external stimulus, such as a painful stimulus, i.e. nociception, due to some surgical event. Such a response may result if the level of analgesia is insufficient. If stimulation continues and no additional analgetic drugs are administered, it is highly likely that the level of hypnosis eventually starts to lighten. EMG can thus provide a rapid indication of impending arousal. Importantly, because of the higher frequency of the EMG data signal, the sampling time can be significantly shorter than that required for the lower frequency EEG signal data. This allows the EMG data to be computed more frequently so that the overall diagnostic indicator can quickly indicate changes in the state of the patient.
In one embodiment of the invention, the EEG signal data and the EMG signal data are separately analyzed and thereafter combined into a diagnostic index or indicator. As noted above, because of the celerity with which changes in the anesthetic state of the patient can be determined from the EMG data, the overall index can quickly inform the anesthesiologist of changes in the state of the patient.
In a further embodiment of the present invention, the spectral range of the complexity computations, i.e. entropy computations, is widened to extend into the EMG range. Thus, the spectral range over which the complexity computations are carried out to provide an indicator may extend from some lower frequency of, for example 0.5 Hz, up to a frequency above 32 Hz. For example, in an embodiment in which the spectral range extends to approximately 47 Hz, a lower frequency band (0.5-32 Hz) will contain mostly EEG signal data while the upper band (32-47 Hz) will include primarily EMG activity. The use of a widened frequency range does not require a division of the spectrum into two segments as does the first embodiment because all components in the widened frequency range are treated in the same manner.
Further, the complexity measurement obtained in this second embodiment of the invention can be updated as often as is permitted by the higher frequencies of the EMG signal data in the widened spectral range of the complexity computation. This will provide a very current indication to the anesthesiologist of the depth of anesthesia of the patient.
The indicator obtained from the signal complexity computation over the widened spectral range can be used in conjunction with a complexity measurement obtained only from the EEG portions of the frequency spectrum to provide useful information to the anesthesiologist regarding what portion of the indicator comes from cerebral activity and what portion comes from muscle activity.
In a more generalized version of the further embodiment of the invention, a set of frequency components obtained from selected frequency ranges is utilized in determining entropy. The patient data from which the frequency components are extracted are obtained from time windows of differing lengths. For lower frequency components, a longer time window is used. For higher frequency components, a shorter time window is used. By selecting the length of each time window to correspond to a particular frequency range, so that not more than the necessary amount of existing, historical data is used, the resulting complexity measures will have optimally fast response times.
Various other features, objects, and advantages of the invention will be made apparent from the following detailed description and the drawings.